Analytics for Mobile Monetization

Analytics for Mobile Monetization is contributing strongly to the massive shift and change within the gaming industry. According to industry analyst Colin Sebastian for RW Baird, video games are expected to reach US$80 billion by 2014. This trend will be parallel to continued growth in video games played on the computer and to explosive growth in mobile games, with the latter expected to hit US$16 billion in revenues by 2016, according to ABI Research.

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Gaming analytics is represented by to vendors such as Google Analytics. The testing of free-to-play models and the freemium model integrate with social gaming. Facebook Credits and games are examples of companies offering hybrid models, optimization engines and vendors which provide analytics and social data. Social games such as Facebook offered on Android mobile platforms, are generating significant revenues. The opportunity for developers to experience the shift in mobile monetization is present for applying such analytics techniques for mobile gaming.

• Location of data and is limited by the fact that you can't perform joins across shards. You must maintain schemas for each server.

• Denormalization is another method that involves grouping and indexing redundant data and often results in latency and issues with maintaining concurrency in relational database systems.

• Distributed caching, which caches recent data in memory, is useful when data is needed. The application (web, game, social network, search engine, and so on) first checks a distributed caching system, such as memcached, for the needed data instead of going back to the relational database.

*IBM

Social analytics starts with listening. Being able to be very good at capturing the specific data monitored before you can conduct analysis. It's interesting that the type of people who "connect" with you are more important than the statistical numbers ie "followers" This is the future, listening to your business ecosystem in it's entirety developing the right social media strategies. With the right strategy in place, the value is powerful. New technology allows for more complex field work and using predictive analytics drives more growth and change in the industry. Quality of data should be important in this industry. The more sophisticated the more need for predictive as much is then determined in "real time."